Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike's information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk.Peer reviewe

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Epigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. Here we examine the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs) in 4 independent cohorts (overall n = 2365). We use Akaike’s information criterion to test which factors best explain variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E), genotypes in cis (G), or their additive (G + E) or interaction (GxE) effects. Genetic and environmental factors in combination best explain DNAm at the majority of VMRs. The CpGs best explained by either G, G + E or GxE are functionally distinct. The enrichment of genetic variants from GxE models in GWAS for complex disorders supports their importance for disease risk

ChromDMM: a Dirichlet-multinomial mixture model for clustering heterogeneous epigenetic data

Publisher Copyright: © 2022 The Author(s). Published by Oxford University Press.Motivation: Research on epigenetic modifications and other chromatin features at genomic regulatory elements elucidates essential biological mechanisms including the regulation of gene expression. Despite the growing number of epigenetic datasets, new tools are still needed to discover novel distinctive patterns of heterogeneous epigenetic signals at regulatory elements. Results: We introduce ChromDMM, a product Dirichlet-multinomial mixture model for clustering genomic regions that are characterized by multiple chromatin features. ChromDMM extends the mixture model framework by profile shifting and flipping that can probabilistically account for inaccuracies in the position and strand-orientation of the genomic regions. Owing to hyper-parameter optimization, ChromDMM can also regularize the smoothness of the epigenetic profiles across the consecutive genomic regions. With simulated data, we demonstrate that ChromDMM clusters, shifts and strand-orients the profiles more accurately than previous methods. With ENCODE data, we show that the clustering of enhancer regions in the human genome reveals distinct patterns in several chromatin features. We further validate the enhancer clusters by their enrichment for transcriptional regulatory factor binding sites.Peer reviewe

Toward a Common Coordinate Framework for the Human Body.

Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.This publication is part of the Human Cell Atlas. We gratefully acknowledge Richard Conroy, Ajay Pillai, Zorina Galis, and Katy Borner for generous feedback and discussion. This work was supported by the Human Biomolecular Atlas Project (NIH 1OT2OD026673- 01), NIH New Innovator Award (1DP2HG009623- 01), the Chan Zuckerberg Initiative (HCA2-A-1708-02755) and an NSF Graduate Fellowship (DGE1342536; A.B.). AR was additionally supported by the NIH BRAIN Initiative, Howard Hughes Medical Institute, and the Klarman Cell Observatory. S.G. is supported by a Royal Society Newton International Fellowship (NIF\R1\181950; E.F. is supported by the Wellcome Trust Mathematical Genomics and Medicine PhD programme (WT/215183/Z/19/Z). J.C.M. acknowledges core support from EMBL and from Cancer Research UK (C9545/A29580)

Pazopanib in metastatic soft tissue sarcoma (STS): Results of a multi-institutional observational study

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ggally: v1.1.0

GGally 1.1.0 ggcoef - New! plot model coefficients with broom and ggplot2 PR#162 Plotting model coefficients (http://www.r-statistics.com/2010/07/visualization-of-regression-coefficients-in-r/) gglegend - New! pull out the legend of a plot which can also be used in ggpairs PR#155, PR#169 ggally_densityDiag fixed bug where '...' was not respected (d0fe633) ggally_smooth added 'method' parameter (411213c) ggally_ratio Does not call ggfluctuation2 anymore. PR#165 ggcorr fixed issue with unnamed correlation matrix used as input PR#146 fixed issue undesired shifting when layout.exp was > 0 PR#171 ggfluctuation2 is being deprecated. Please use ggally_ratio instead PR#165 ggnetworkmap fixed issue with overlaying network on a world map PR#157 ggparcoord Fixed odd bug where a list was trying to be forced as a double PR#162 ggpairs Fixed improperly rotated axes with ggally_ratio PR#165 ggscatmat added 'corMethod' parameter for use in upper triangle PR#145 ggsurv size.est and size.ci parameters added PR#153 ordering changed to reflect survival time PR#147 added a vignette PR#154 wrap documentation updated PR#152 changes default behavior only. If an argument is supplied, the argument will take precedence github chat https://gitter.im/ggobi/ggally is the place to visit for general questions. travis-ci cache packages for faster checking install covr and lintr from github for testing purpose

ggobi/ggally: v1.3.2

GGally 1.3.2 ggpairs and ggduo Removed warning where pure numeric names gave a warning (#238, @lepennec) Fixed ordering issue with horizontal boxplots (#239) ggparcoord Fixed missing x aes requirement when shadebox is provided (#237, @treysp) Package Made igraph a non required dependency for tests (#240

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit